A novel structured argumentation framework for improved explainability of classification tasks
This work addresses the need for more understandable and concise argumentative models in classification tasks, particularly for knowledge discovery and refinement, though it appears incremental as an extension of existing argumentative decision graphs.
The paper tackled the problem of improving explainability in classification tasks by proposing a novel structured argumentation framework called xADG, which achieved strong balanced accuracy and reduced the average number of supports needed for conclusions.
This paper presents a novel framework for structured argumentation, named extend argumentative decision graph ($xADG$). It is an extension of argumentative decision graphs built upon Dung's abstract argumentation graphs. The $xADG$ framework allows for arguments to use boolean logic operators and multiple premises (supports) within their internal structure, resulting in more concise argumentation graphs that may be easier for users to understand. The study presents a methodology for construction of $xADGs$ and evaluates their size and predictive capacity for classification tasks of varying magnitudes. Resulting $xADGs$ achieved strong (balanced) accuracy, which was accomplished through an input decision tree, while also reducing the average number of supports needed to reach a conclusion. The results further indicated that it is possible to construct plausibly understandable $xADGs$ that outperform other techniques for building $ADGs$ in terms of predictive capacity and overall size. In summary, the study suggests that $xADG$ represents a promising framework to developing more concise argumentative models that can be used for classification tasks and knowledge discovery, acquisition, and refinement.